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feat(pygal): implement indicator-rsi (#3265)
## Implementation: `indicator-rsi` - pygal Implements the **pygal** version of `indicator-rsi`. **File:** `plots/indicator-rsi/implementations/pygal.py` **Parent Issue:** #3229 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20795216382)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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‎plots/indicator-rsi/implementations/pygal.py‎

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""" pyplots.ai
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indicator-rsi: RSI Technical Indicator Chart
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Library: pygal 3.1.0 | Python 3.13.11
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Quality: 85/100 | Created: 2026-01-07
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"""
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import numpy as np
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import pygal
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from pygal.style import Style
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# Data - Generate realistic RSI values over 120 trading days
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np.random.seed(42)
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n_days = 120
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lookback = 14
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# Generate price changes that produce RSI entering both overbought (>70) and oversold (<30) zones
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# Use stronger trending to ensure RSI reaches extreme values
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base_changes = np.random.randn(n_days) * 0.8
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# Strong uptrend periods (push RSI above 70) - increased magnitude
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base_changes[15:32] += 4.0 # Strong bullish phase
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base_changes[75:92] += 4.5 # Another strong bullish phase
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# Strong downtrend periods (push RSI below 30) - increased magnitude
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base_changes[40:57] -= 4.0 # Strong bearish phase
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base_changes[100:115] -= 3.5 # Another bearish phase
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price_changes = base_changes
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# Calculate RSI using exponential moving average
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gains = np.where(price_changes > 0, price_changes, 0)
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losses = np.where(price_changes < 0, -price_changes, 0)
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# Initialize EMA
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avg_gain = np.zeros(n_days)
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avg_loss = np.zeros(n_days)
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# First average
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avg_gain[lookback - 1] = np.mean(gains[:lookback])
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avg_loss[lookback - 1] = np.mean(losses[:lookback])
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# EMA for subsequent values
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alpha = 1 / lookback
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for i in range(lookback, n_days):
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avg_gain[i] = alpha * gains[i] + (1 - alpha) * avg_gain[i - 1]
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avg_loss[i] = alpha * losses[i] + (1 - alpha) * avg_loss[i - 1]
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# Calculate RSI (avoid division by zero)
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with np.errstate(divide="ignore", invalid="ignore"):
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rs = np.divide(avg_gain, avg_loss, out=np.full_like(avg_gain, 100.0), where=avg_loss > 0)
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rsi = 100 - (100 / (1 + rs))
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rsi[:lookback] = 50 # Fill initial values with neutral
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# Custom style for large canvas
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custom_style = Style(
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background="white",
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plot_background="white",
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foreground="#333333",
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foreground_strong="#333333",
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foreground_subtle="#666666",
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colors=(
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"#D04040", # Overbought line (red)
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"#30A030", # Oversold line (green)
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"#888888", # Centerline (gray)
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"#306998", # RSI line (blue)
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),
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title_font_size=80,
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label_font_size=52,
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major_label_font_size=46,
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legend_font_size=52,
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value_font_size=40,
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stroke_width=6,
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opacity=0.95,
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opacity_hover=1.0,
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)
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# Create chart with custom configuration for zone rendering
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chart = pygal.Line(
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width=4800,
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height=2700,
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style=custom_style,
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title="indicator-rsi · pygal · pyplots.ai",
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x_title="Trading Period (120 days, 14-period RSI lookback)",
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y_title="RSI Value (0-100)",
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show_dots=False,
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show_x_guides=False,
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show_y_guides=True,
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range=(0, 100),
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interpolate="cubic",
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legend_at_bottom=True,
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legend_box_size=40,
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margin=60,
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margin_bottom=180,
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show_x_labels=False,
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)
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# Create RSI data with zone-based coloring approach
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# Since pygal doesn't support horizontal bands natively, we'll use secondary value lines
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# and visual indication through dashed threshold lines with clear colors
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# Add threshold lines (constant values across all days)
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overbought_line = [70] * n_days
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oversold_line = [30] * n_days
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centerline = [50] * n_days
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# Add overbought threshold line (red, dashed)
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chart.add("Overbought (70)", overbought_line, stroke_style={"width": 5, "dasharray": "20,10"}, show_dots=False)
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# Add oversold threshold line (green, dashed)
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chart.add("Oversold (30)", oversold_line, stroke_style={"width": 5, "dasharray": "20,10"}, show_dots=False)
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# Add centerline (gray, dotted)
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chart.add("Centerline (50)", centerline, stroke_style={"width": 3, "dasharray": "10,10"}, show_dots=False)
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# Add RSI data last so it appears on top with thicker line
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chart.add("RSI (14)", list(rsi), stroke_style={"width": 8}, show_dots=False)
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# Save as PNG and HTML
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chart.render_to_png("plot.png")
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chart.render_to_file("plot.html")

‎plots/indicator-rsi/metadata/pygal.yaml‎

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library: pygal
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specification_id: indicator-rsi
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created: '2026-01-07T20:21:37Z'
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updated: '2026-01-07T20:44:20Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20795216382
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issue: 3229
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python_version: 3.13.11
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library_version: 3.1.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/indicator-rsi/pygal/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/indicator-rsi/pygal/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/indicator-rsi/pygal/plot.html
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quality_score: 85
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review:
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strengths:
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- Y-axis correctly fixed 0-100 with all three reference lines (70, 30, 50) as horizontal
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thresholds
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- RSI data demonstrates excellent feature coverage entering both overbought and
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oversold zones multiple times
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- Proper RSI calculation using EMA method with 14-period lookback
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- Good use of pygal Style customization for large canvas rendering
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- Clean smooth cubic interpolation makes the RSI trend easy to follow
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weaknesses:
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- Missing zone shading/highlighting for overbought (70-100) and oversold (0-30)
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regions as suggested in spec notes
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- Red/green color scheme for threshold lines could be improved for colorblind accessibility
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image_description: 'The plot displays an RSI (Relative Strength Index) technical
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indicator chart rendered as a smooth blue line (cubic interpolation) showing RSI
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values over 120 trading periods. The Y-axis is correctly fixed from 0 to 100.
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Three horizontal reference lines are present: a red dashed line at 70 (overbought
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threshold), a green dashed line at 30 (oversold threshold), and a gray dotted
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line at 50 (centerline). The RSI line clearly enters both overbought territory
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(peaking at ~97) and oversold territory (dipping to ~14), demonstrating the full
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range of RSI momentum behavior. The title "indicator-rsi · pygal · pyplots.ai"
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is displayed at the top. A legend at the bottom identifies all four elements with
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appropriate color coding. The layout has good proportions with the plot filling
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the canvas well.'
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criteria_checklist:
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visual_quality:
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score: 34
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max: 40
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items:
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- id: VQ-01
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name: Text Legibility
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score: 9
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max: 10
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passed: true
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comment: Title, axis labels, and legend text are clearly readable with appropriately
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scaled font sizes for the 4800x2700 canvas
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- id: VQ-02
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name: No Overlap
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score: 8
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max: 8
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passed: true
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comment: No overlapping text elements; legend at bottom is well-spaced
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- id: VQ-03
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name: Element Visibility
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score: 7
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max: 8
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passed: true
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comment: RSI line is clearly visible with good stroke width; threshold lines
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appropriately styled as dashed
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- id: VQ-04
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name: Color Accessibility
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score: 4
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max: 5
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passed: true
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comment: Red/green threshold lines could be problematic for colorblind users,
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though dashed styling helps differentiate
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- id: VQ-05
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name: Layout Balance
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score: 4
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max: 5
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passed: true
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comment: Good canvas utilization; slight excess whitespace at bottom due to
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legend placement
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- id: VQ-06
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name: Axis Labels
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score: 2
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max: 2
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passed: true
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comment: Y-axis labeled with range info; X-axis labeled with trading period
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context
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- id: VQ-07
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name: Grid & Legend
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score: 0
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max: 2
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passed: false
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comment: Y-guides visible but grid lines are somewhat sparse
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spec_compliance:
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score: 21
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max: 25
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items:
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- id: SC-01
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name: Plot Type
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score: 8
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max: 8
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passed: true
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comment: Correct line chart for RSI oscillator
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- id: SC-02
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name: Data Mapping
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score: 5
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max: 5
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passed: true
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comment: RSI values correctly plotted on 0-100 scale over time
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- id: SC-03
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name: Required Features
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score: 3
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max: 5
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passed: false
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comment: Has threshold lines at 30, 70, and 50; missing zone shading for overbought/oversold
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regions
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- id: SC-04
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name: Data Range
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score: 3
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max: 3
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passed: true
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comment: Y-axis shows full 0-100 range as required
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- id: SC-05
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name: Legend Accuracy
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score: 2
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max: 2
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passed: true
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comment: Legend correctly identifies all elements
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- id: SC-06
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name: Title Format
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score: 2
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max: 2
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passed: true
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comment: Uses correct format indicator-rsi · pygal · pyplots.ai
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data_quality:
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score: 18
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max: 20
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 8
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max: 8
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passed: true
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comment: RSI enters both overbought (>70, peaks ~97) and oversold (<30, dips
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~14) zones multiple times
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- id: DQ-02
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name: Realistic Context
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score: 5
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max: 7
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passed: true
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comment: Trading context appropriate; RSI calculation uses proper EMA method
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with 14-period lookback
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- id: DQ-03
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name: Appropriate Scale
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score: 5
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max: 5
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passed: true
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comment: RSI values mathematically correct (0-100 bounded) with realistic
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momentum patterns
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code_quality:
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score: 9
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max: 10
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items:
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- id: CQ-01
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name: KISS Structure
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score: 3
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max: 3
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passed: true
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comment: 'Clean linear structure: imports → data → RSI calculation → chart
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→ save'
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- id: CQ-02
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name: Reproducibility
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score: 3
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max: 3
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passed: true
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comment: Uses np.random.seed(42)
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- id: CQ-03
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name: Clean Imports
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score: 2
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max: 2
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passed: true
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comment: Only necessary imports (numpy, pygal, Style)
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- id: CQ-04
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name: No Deprecated API
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score: 1
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max: 1
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passed: true
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comment: Uses current pygal API
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- id: CQ-05
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name: Output Correct
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score: 0
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max: 1
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passed: false
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comment: Saves as plot.png and plot.html correctly
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library_features:
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score: 3
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max: 5
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items:
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- id: LF-01
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name: Distinctive Features
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score: 3
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max: 5
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passed: true
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comment: Custom Style with scaled fonts, stroke_style for dashed lines, cubic
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interpolation; interactive tooltips not fully leveraged
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verdict: APPROVED
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impl_tags:
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dependencies: []
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techniques:
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- html-export
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patterns:
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- data-generation
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- iteration-over-groups
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dataprep:
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- rolling-window
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styling:
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- alpha-blending
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- grid-styling

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